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Big data seminar (TICT(CSE)BATCH--> 2013-2017)

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*********TECHNO INDIA COLLEGE OF TECHNOLOGY**********
RAJARHAT,KOLKATA - 700156
A full powerpoint presentation on big data analytics and Hadoop. This is made by: SK IBRAHIM ANAM , SOUVIK JANA.
Designed by:
SK IBRAHIM ANAM
Group Members:
SOUVIK JANA.
SK IBRAHIM ANAM.
VISHAL KUMAR.

Published in: Data & Analytics
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Big data seminar (TICT(CSE)BATCH--> 2013-2017)

  1. 1. WHAT IS Data is raw, unorganized facts that need to be processed. Data can be something simple, seemingly random and of itself worthless useless until it is organized.
  2. 2. DIFFERENT TYPES OF DATA Traditional RDBMS deals with only Structured Data Need of a Technology which deals with Semi – Structured Data ,Unstructured Data and Structured Data as well Semi-Structured Data
  3. 3. Traditional Concept of Data Storage Organizations Banking Sector Stock Exchange Hospital Social Media Online Shopping Others Extract Data Transform Data Load into DataBase End Users Generate Reports & Perform Analytics Managing Data Processing Data Data GrowsDifficult
  4. 4. Drawback of Using Traditional Approach Expensive Time Consuming Scalability Storage Size Resource Failure
  5. 5. The Model of Generating or Consuming Data Has Change... OLD MODEL - Few companies are generating the data, all other consuming the data. NEW MODEL -All of us generating the data, and all of us consuming the data.
  6. 6. BIG DATA
  7. 7. WHAT IS Big data means really a Big Data, it is a collection of large datasets that cannot be processed using traditional computing techniques. It requires new architecture , new techniques , various tools and frameworks .
  8. 8. Definition of BIG DATA
  9. 9. Different Sources of DATA SOURCES
  10. 10. WHERE THE BIG DATA IS USED IT Industries Manufacturing Industries Telecommunications Banking sector Healthcare
  11. 11. CHALLENGES IN HANDLING BIG DATA There are two main challenges in handle BIG DATA 1. How do we store and manage such a huge volume of DATA, efficiently. 2. How do we process & extract valuable information from the huge volume of DATA within a given time frame.
  12. 12. BRIEF HISTORY OF HADOOP
  13. 13. WHAT IS Hadoop is a open Source Framework. It is designed to store and Process huge volume of Data, efficiently. Hadoop is a platform that provides both distributed storage and computational capabilities.
  14. 14. Why HADOOP Is Used
  15. 15. MAJOR COMPONENT S OF HADOOP ECOSYSTEM HADOOP COMPONENTS HADOOP DISTRIBUTED FILE SYSTEM Google MAPREDUCE ALGORITHM Storage Processing
  16. 16. HADOOP ECOSYSTEM Flume Sqoop Semi-Structured or Unstructured Data Structured Data Import or Export
  17. 17. Features of Hadoop Cost Effective System (Use Commodity Machine) Large Cluster of Nodes (Processing Power & Storage Capacity is Increase)
  18. 18. Features of Hadoop Parallel Processing (Less Time is Required to Store & Access the Data) Distributed Data (Data is Distributed in Different Nodes)
  19. 19. Features of Hadoop Automatic Failover Management Heterogeneous Cluster
  20. 20. Features of Hadoop Scalability
  21. 21. How The Data Is Stored In Hadoop Clusters Rack 1 Rack 2 Node 1 Node 4Node 3Node 2
  22. 22. Hadoop Distributed File System Name Node Task Tracker Client Block5 Block2 Block4 Block3 Block1 Block6 Data Node Block4 Block1 Block3 Block2 Block6 Block5 Block1 Block2 Block3 Block4 Block5 Block6 Data Node Data Node Task Tracker Task Tracker Job Tracker Secondary Name Node
  23. 23. MapReduce Flow
  24. 24. MapReduce Framework Map Reduce works by breaking the processing into two phases Map Phase & Reduce Phase Input Split Map Reduce Output Shuffle & Sort
  25. 25. Disadvantages Security Concerns Not Fit For Small Data
  26. 26. Future Scope of Big Data & Hadoop
  27. 27. Conclusion...
  28. 28. Source of Information Google
  29. 29. Presented By Vishal Kumar Sk Ibrahim Anam Souvik Jana
  30. 30. Thank You

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